1. Field of the Invention
This invention relates to a technique of recognizing a moving object such as a person or an automobile in a moving image and in particular to a technique capable of avoiding confusion between moving objects and precisely tracking and recognizing each moving object.
2. Description of the Related Technique
A moving image photographed by a camera is used in various applications such as tracking the motion of each customer in a store and managing efficient commodity product placement in addition to security management.
The usages of the moving image include not only a mode in which an image is simply monitored and recorded, but also a mode in which the moving object such as a person shown in the moving image is recognized and tracked for monitoring and recording the motion of the moving object. For example, a region in which some motion exists is detected and photographed by a video camera provided on a ceiling and the person shown in the photographed moving image is tracked.
For example, a technique of extracting and tracking persons from the image photographed by a plurality of cameras and counting the number of persons passing through a predetermined determination line so that the number of persons can be precisely measured without being affected by back-and-force and side-to-side person overlap is known. (Refer to JP-A-10-049718. )
For example, the following technique is known as a technique that can be used for tracking persons in a moving image:
A technique of detecting a skin color region in an image, thereby recognizing the required portion of the face of a person, etc., is known; the technique can be used to detect a person in an image and the face of each person. (Refer to JP-A-2000-105819. )
Processing of recognizing the object image of the face of a person, etc., can be performed by determining the similarity degree between the object image and a provided reference image. A technique is known wherein once a face is detected from a moving image frame, a face image subjected to parallel move or rotation move from the position of the face is generated and the similarity degree between the generated face image and a reference image is determined; the technique can be used to detect the face of the person in the image (further the person). (Refer to JP-A-2002-269546. )
Normalization processing of converting an object image geometrically under a predetermined condition is previously performed, whereby the number of the reference images for comparison can be lessened. As for the normalization processing, a technique of detecting the difference between the image to be processed and the image after subjected to normalization based on an illumination pattern and performing normalization processing using the detection result is known. One example of such conventional technique is disclosed in: “Rotation Invariant Neural Network-Based Face Detection, H. A. Rowly, S. Baluja, and T. Kanade Proceedings of IEEE Conference on Comp-44”, which will be referred to as “Rotation Invariant Neural Network”.
A technique of extracting feature points from a face image and making a comparison with a provided template based on the feature points to conduct personal authentication from the face image is known; the technique can be used to determine whether or not two face images are identical. One example of such conventional technique is disclosed in: “Laurens Wiskott, Jean-Marc Fellous, Norbert Krouger and Christoph von der Malsburg, Face Recognition by Elastic Bunch Matching Proc. 7th Intern. Conf. on Computer Analysis of Image and Patterns 1997”, which will be referred to as “Face Recognition by Elastic Bunch Matching”.
A technique of mapping pattern information representing a face into a predetermined space so as to increase the individual difference and making a comparison with a provided template in the space to conduct personal authentication from the face image is known; the technique can be used to determine whether or not two face images are identical. One example of such conventional technique is disclosed in: “Matthew A. Turk and Alex P. Pentland, Eigenfaces for Recognition Journal of Cognitive Neuroscience Vol. 3, No. 1, pp. 71-86 (1991)”, which will be referred to as “Eigenfaces for Recognition”.
A technique of identifying the individual using the individual difference of the three-dimensional shape of the face rather than conducting personal authentication based on the shapes and placement of the feature portions of the eyes, nose, mouth, etc., of the face is known; the technique can be used to determine whether or not two face images are identical. (Refer to JP-A-2-311962. )